README.md

---
license: other
license_name: helpingai
license_link: https://huggingface.co/OEvortex/HelpingAI2.5-5B/blob/main/LICENSE.md
pipeline_tag: text-generation
language:
- en
tags:
- HelpingAI
- Emotionally-Intelligent
- EQ-focused
- Conversational
- SLM
- llama-cpp
- gguf-my-repo
base_model: OEvortex/HelpingAI2.5-10B
---

# Triangle104/HelpingAI2.5-10B-Q5_K_S-GGUF
This model was converted to GGUF format from [`OEvortex/HelpingAI2.5-10B`](https://huggingface.co/OEvortex/HelpingAI2.5-10B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/OEvortex/HelpingAI2.5-10B) for more details on the model.

---
Model details:
-
HelpingAI2.5-10B is a compact 
yet powerful language model specifically designed for emotionally 
intelligent conversations and human-centric interactions.





	
		
	

		🎯 Key Highlights
	



Architecture: 10B parameter transformer-based model
Training Focus: Emotional intelligence and empathetic responses
Emotion Score: Achieves 98.13 on standardized emotional intelligence tests
Deployment: Optimized for efficient deployment on consumer hardware

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/HelpingAI2.5-10B-Q5_K_S-GGUF --hf-file helpingai2.5-10b-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/HelpingAI2.5-10B-Q5_K_S-GGUF --hf-file helpingai2.5-10b-q5_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/HelpingAI2.5-10B-Q5_K_S-GGUF --hf-file helpingai2.5-10b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/HelpingAI2.5-10B-Q5_K_S-GGUF --hf-file helpingai2.5-10b-q5_k_s.gguf -c 2048
```